Bibliographic record
Abstract
Focus Tar sands and oil shale are “unconventional” oil resources. Unconventional oil resources are characterized by their solid, or near-solid, state under reservoir conditions, which requires new, and sometimes unproven, technology for their recovery. For tar sands the hydrocarbon is a highly viscous bitumen; for oil shale, it is a solid hydrocarbon called “kerogen.” Unconventional oil resources are found in greater quantities than conventional petroleum, and will play an increasingly important role in liquid fuel supply as conventional petroleum becomes harder to produce. With the commercial success of Canadian tar-sand production, and the proving of technology, these unconventional resources are increasingly becoming “conventional.” This chapter focuses on the trends that drive increased production from tar sands and oil shale, and discusses the geological, technical, environmental, and fiscal issues governing their development. Synopsis Oil shale and tar sands occur in dozens of countries around the world. With in-place resources totaling at least 4 trillion barrels (bbl), they exceed the world's remaining petroleum reserves, which are probably less than 2 trillion bbl. As petroleum becomes harder to produce, oil shale and tar sands are finding economic and thermodynamic parity with petroleum. Thermodynamic parity, e.g., similarity in the energy cost of producing energy, is a key indicator of economic competitiveness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".